Ignacy Misztal Professor Animal & Dairy Science

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Portrait of Ignacy Misztal

Biography

Ignacy Misztal,
D.W. Brooks Distinguished Professor of Animal Breeding and Genetics

Dr. Ignacy Misztal came to UGA as Associate Professor in 1996. He obtained his MSEE from Warsaw Technical University and his PhD from Polish Academy of Science. Prior to coming to UGA, he stayed at Warsaw Agriculture University (Poland), Hohenheim University (Germany), University of Guelph (Canada), and University of Illinois at Urbana-Champaign.

Dr. Misztal main research is in genetic evaluation methodology and computational strategies that improve livestock production and sustainability. He pioneered single-step GBLUP - a method to consider genomic, pedigree and phenotypic information jointly; this method
become the standard in the field. He also pioneered a novel method to evaluate animals for heat tolerance using special models with data from public weather stations. Software programs developed by his lab have been used by researchers in at least 50 countries and have become the backbone for the genetic evaluation programs of some of the largest animal genetics companies and breed associations in the world.

Dr. Misztal’s work has published 232 refereed journal papers, delivered over 135 invited talks in 32 countries, and has taught 30 short courses in 16 countries on 6 continents. His research has attracted over $9 million in extramural funding and is critical to the dairy, beef, pig, and poultry industries globally. His awards include J. L. Lush (ADSA), Rockefeller Prentice (ASAS), Research (NAAB), Pioneer (BIF), and D.W. Brooks (Research, Global Programs, Distinguished Professor -- CAES UGA).

Education
1978 M.S. (Computer Engineering), Warsaw Technical University
1985 Ph.D. (Technical Sciences), Polish Academy of Sciences

Research interests
Genetic evaluation methodology
Programming algorithms related to animal breeding
Genetics of heat tolerance, fertility, and mortality
Genotype x environment interaction and competition effects
Incorporation of genomic information into genetic evaluations

Group website: http://nce.ads.uga.edu
Personal website: http://nce.ads.uga.edu/~ignacy
Researchgate: https://www.researchgate.net/profile/Ignacy_Misztal
Googlescholar: https://scholar.google.com/citations?user=Gl9HDqYAAAAJ&hl=en

Courses Taught

Mixed Models
Computing in Animal Breeding
Advanced Animal Breeding
Graduate student seminar

Selected Recent Publications

Aguilar, I., I. Misztal, S. Tsuruta, G.R. Wiggans, and T.J. Lawlor. 2011. Multiple trait genomic evaluation of conception rate in Holsteins. J. Dairy Sci. 94:2621–2624.

Perez-Enciso, M., and I. Misztal. 2011. Qxpak.5: Old mixed model solutions for new genomics problems. BMC Bioinformatics 12:202.

Simeone, R., I. Misztal, I. Aguilar, and A. Legarra. 2011. Evaluation of the utility of genomic relationship matrix as a diagnostic tool to detect mislabeled genotyped animals in a broiler chicken population. J, Anim. Breed. Genet. 128:386–393.

Chen, C.Y., I. Misztal, I. Aguilar, A. Legarra, and B. Muir. 2011. Effect of different genomic relationship matrices on accuracy and scale. J. Anim. Sci. 89:2673–2679.

Tsuruta, S., I. Aguilar, I. Misztal, and T.J. Lawlor. 2011. Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. J. Dairy Sci. 94:4198–4204.

Vitezica, Z. G., I. Aguilar, I. Misztal, and A. Legarra. 2011. Bias in genomic predictions for populations under selection. Genet. Res. Camb. 93:357–366.

Simeone, R., I. Misztal, I. Aguilar, and Z. Vitezica. 2012. Evaluation of a multi-line broiler chicken population using a single-step genomic evaluation procedure. J. Anim. Breed. Genet. 129:3–10.

Bloemhof, S., A. Kause, E. Knol, J. van Arendonk, and I. Misztal. 2012. Heat stress effects on farrowing rate in sows: Genetic parameter estimation using within-line and crossbred models. J. Anim. Sci. 90:2109–2119.

Wang, H., I. Misztal, I. Aguilar, A. Legarra, and W.M. Muir. 2012. Genome-wide association mapping including phenotypes from relatives without genotypes. Genet. Res. 94:73–83.

Misztal, I., S. Tsuruta, I. Aguilar, A. Legarra, P.M. VanRaden, and T.J. Lawlor. 2013. Methods to approximate reliabilities in single-step genomic evaluation. J. Dairy Sci. 96:647–654.

Misztal, I., Z.G. Vitezica, A. Legarra, I. Aguilar, and A.A. Swan. 2013. Unknown-parent groups in single-step genomic evaluation. J. Anim. Breed. Genet. 130:252–258.

Tsuruta, S., I. Misztal, and T.J. Lawlor. 2013. Short communication: Genomic evaluations of final score for US Holsteins benefit from the inclusion of genotypes on cows. J. Dairy Sci. 96:3332–3335.

Misztal, I., S.E. Aggrey, and W.M. Muir. 2013. Experiences with a single-step genome evaluation. Poult. Sci. 92:2530–2534.

Lourenco, D.A.L., I. Misztal, H. Wang, I. Aguilar, S. Tsuruta, and J.K. Bertrand. 2013. Prediction accuracy for a simulated maternally affected trait of beef cattle using different genomic evaluation models. J. Anim. Sci. 91:4090–4098.

Dufrasne, M., I. Misztal, S. Tsuruta, J. Holl, K.A. Gray, and N. Gengler. 2013. Estimation of genetic parameters for birth weight, preweaning mortality, and hot carcass weight of crossbred pigs. J. Anim. Sci. 91:5565–5571.

Elzo, M.A., C.A. Martinez, G.C. Lamb, D.D. Johnson, M.G. Thomas, I. Misztal, D.O. Rae, J.G. Wasdin, and J.D. Driver. 2013. Genomic-polygenic evaluation for ultrasound and weight traits in Angus–Brahman multibreed cattle with the Illumina3k chip. Livest. Sci. 153:39–49.

Lourenco, D.A.L., I. Misztal, S. Tsuruta, I. Aguilar, E. Ezra, M. Ron, A. Shirak, and J.I. Weller. 2014. Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses. J. Dairy Sci. 97:1742–1752.

Misztal, I., A. Legarra, and I. Aguilar. 2014. Using recursion to compute the inverse of the genomic relationship matrix. J. Dairy Sci. 97:3943–3952.

Lourenco, D.A.L., I. Misztal, S. Tsuruta, I. Aguilar, T.J. Lawlor, S. Forni, and J.I. Weller. 2014. Are evaluations on young genotyped animals benefiting from the past generations? J. Dairy Sci. 97:3930–3942.

Tokuhisa, K., S. Tsuruta, A. De Vries, J.K. Bertrand, and I. Misztal. 2014. Estimation of regional genetic parameters for mortality and 305-d milk yield of US Holsteins in the first 3 parities. J. Dairy Sci. 97:4497–4502.

Tsuruta, S., I. Misztal, D.A.L. Lourenco, and T.J. Lawlor. 2014. Assigning unknown parent groups to reduce bias in genomic evaluations of final score in US Holsteins. J. Dairy Sci. 97:5814–5821.

Wang, H., I. Misztal, I. Aguilar, A. Legarra, R.L. Fernando, Z. Vitezica, R. Okimoto, T. Wing, R. Hawken, and W.M. Muir. 2014. Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens. Front. Genet. 5:134.  

Legarra, A., O.F. Christensen, I. Aguilar, and I. Misztal. 2014. Single Step, a general approach for genomic selection. Livest. Sci. 166:54–65.

Dufrasne, M., I. Misztal, S. Tsuruta, N. Gengler, and K.A. Gray. 2014. Genetic analysis of pig survival up to commercial weight in a crossbred population. Livest. Sci. 167:19–24.

Wang, H., I. Misztal, and A. Legarra. 2014. Differences between genomic-based and pedigree-based relationships in a chicken population, as a function of quality control and pedigree links among individuals. J. Anim. Breed. Genet. 31:445–451.

Fragomeni, B.O., I. Misztal, D.L. Lourenco, I. Aguilar, R. Okimoto, and W.M. Muir. 2014. Changes in variance explained by top SNP windows over generations for three traits in broiler chicken. Front. Genet. 5:3332.

Zhang, X., I. Misztal, M. Heidaritabar, J.W.M. Bastiaansen, R. Borg, R.L. Sapp, T. Wing, R.R. Hawken, D.A.L. Lourenco, and Z.G. Vitezica. 2014. Prior genetic architecture impacting genomic regions under selection: An example using genomic selection in two poultry breeds. Livest. Sci. 171:1–11.

Forneris, N.S., A. Legarra, Z.G. Vitezica, S. Tsuruta, I. Aguilar, I. Misztal, and R.J.C. Cantet. 2015. Quality control of genotypes using heritability estimates of gene content at the marker. Genetics 199:675–681.

Lukaszewicz, M., R. Davis, J.K. Bertrand, I. Misztal, and S. Tsuruta. 2015. Correlations between purebred and crossbred body weight traits in Limousin and Limousin-Angus populations. J. Anim. Sci. 93:1490–1493.

Fragomeni, B.O., D.A.L. Lourenco, S. Tsuruta, Y. Masuda, I. Aguilar, A. Legarra, T.J. Lawlor, and I. Misztal. 2015. Hot topic: Use of genomic recursions in single-step genomic best linear unbiased predictor (BLUP) with a large number of genotypes. J. Dairy Sci. 98:4090–4094.

Fragomeni, B.O., D.A.L. Lourenco, S. Tsuruta, Y. Masuda, I. Aguilar, and I. Misztal. 2015. Use of genomic recursions and algorithm for proven and young animals for single-step genomic BLUP analyses–A simulation study. J. Anim. Breed. Genet. 132:340–345.

Legarra, A., O.F. Christensen, Z.G. Vitezica, I. Aguilar, and I. Misztal. 2015. Ancestral relationships using metafounders: Finite ancestral populations and across population relationships. Genetics 200:455–468.

Lourenco, D.A.L., S. Tsuruta, B.O. Fragomeni, Y. Masuda, I. Aguilar, A. Legarra, J.K. Bertrand, T.S. Amen, L. Wang, D.W. Moser, and I. Misztal. 2015. Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus. J. Anim. Sci. 93:2653–2662.

Tsuruta, S., D.A.L. Lourenco, I. Misztal, and T.J. Lawlor. 2015. Genotype by environment interactions on culling rates and 305-d milk yield of Holstein cows in three US regions. J. Dairy Sci. 98:5796–805.

Lourenco, D.A.L., B.O. Fragomeni, S. Tsuruta, I. Aguilar, B. Zumbach, R.J. Hawken, A. Legarra, and I. Misztal. 2015. Accuracy of estimated breeding values for males and females with genomic information on males, females, or both: A broiler chicken example. Genet. Sel. Evol. 47:56.

Masuda, Y., S. Tsuruta, I. Aguilar, and I. Misztal. 2015. Technical note: Acceleration of sparse operations for average-information REML analyses with supernodal methods and sparse-storage refinements. J. Anim. Sci. 93:4670–4674.

Misztal, I. 2016. Inexpensive computation of the inverse of the genomic relationship matrix in populations with small effective population size. Genetics 202:411–409.

Masuda, Y., I. Misztal, S. Tsuruta, A. Legarra, I. Aguilar, D.A. Lourenco, B.O. Fragomeni, and T.J Lawlor. 2016. Implementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals. J. Dairy Sci. 99:1968–1974.

Lourenco, D.A.L., S. Tsuruta, B.O. Fragomeni, C.Y. Chen, W.O. Herring, and I. Misztal. 2016. Crossbred evaluations in single-step genomic best linear unbiased predictor using adjusted realized relationship matrices. J. Anim. Sci. 94:909–919.

Vitezica, Z., L. Varona, J.M. Elsen, I. Misztal, W. Herring, and A. Legarra. 2016. Genomic BLUP including additive and dominant variation in purebreds and F1 crossbreds, with an application in pigs. Genet. Sel. Evol. 48:6.

Pocrnic, I., D.A.L. Lourenco, Y. Masuda, and I. Misztal. 2016. The dimensionality of genomic information and its effect on genomic prediction. Genetics 203:573–581.

Vallejo, R.L., T.D. Leeds, B.O. Fragomeni, G. Gao, A.G. Hernandez, I. Misztal, T.J. Welch, G.D. Wiens, and Y. Palti. 2016. Evaluation of genome-enabled selection for bacterial cold water disease resistance using progeny performance data in rainbow trout: Insights on genotyping methods and genomic prediction models. Front. Genet. 7:96.

Silva, R.M.O., B.O. Fragomeni, D.A.L. Lourenco, A.F.B. Magalhäes, N. Irano, R. Carvalheiro, R.C. Canesin, M.E.Z. Mercadante, A.A. Boligon, F.S. Baldi, I. Misztal, and L.G. Albuquerque. 2016. Accuracies of genomic prediction of feed efficiency traits using different prediction and validation methods in an experimental Nelore cattle population. J. Anim. Sci. 94:3613–3623.

van der Heide, E.M.M., D.A.L. Lourenco, C.Y. Chen, W.O. Herring, R.L. Sapp, D.W. Moser, S. Tsuruta, Y. Masuda, B.J. Ducro, and I. Misztal. 2016. Sexual dimorphism in livestock species selected for economically important traits. J. Anim. Sci. 94:3684–3692.

Zhang, X., D. Lourenco, I. Aguilar, A. Legarra, and I. Misztal. 2016. Weighting strategies for single-step genomic BLUP: An iterative approach for accurate calculation of GEBV and GWAS. Front. Genet. 7:151.

Bradford, H.L., B.O. Fragomeni, D.A.L. Lourenco, and I. Misztal. 2016. Genetic evaluations for growth heat tolerance in Angus cattle. J. Anim. Sci. 94:4143–4150.         

Bradford, H.L., B.O. Fragomeni, D.A.L. Lourenco, and I. Misztal. 2016.  Regional and seasonal analyses of weights in growing Angus cattle. J. Anim. Sci. 94:4369–4375.

Pocrnic, I., D.A.L. Lourenco, Y. Masuda, and I. Misztal. 2016. Dimensionality of genomic information and performance of the Algorithm for Proven and Young for different livestock species. Genet. Sel. Evol. 48:82.

Fragomeni, B.O., D.A.L. Lourenco, S. Tsuruta, K. Gray, Y. Huang, and I. Misztal. 2016. Modeling response to heat stress in pigs from nucleus and commercial farms in different locations in the United States. J. Anim. Sci. 94:4789–4798.       

Andonov, S., D.A.L. Lourenco, B.O. Fragomeni, Y. Masuda, I. Pocrnic, S. Tsuruta, and I. Misztal. 2017. Accuracy of breeding values in small genotyped populations using different sources of external information—A simulation study. J. Dairy Sci. 100:395–401.

Fragomeni, B.O., D.A.L. Lourenco, S. Tsuruta, H.L. Bradford, K.A. Gray, Y. Huang, and I. Misztal. 2016. Using single-step genomic best linear unbiased predictor to enhance the mitigation of seasonal losses due to heat stress in pigs. J. Anim. Sci. 12:5004–5013.                    

Misztal, I., and A. Legarra. 2017. Invited review: Efficient computation strategies in genomic selection. Animal 11:731–736.

Masuda, Y., I. Misztal, A. Legarra, S. Tsuruta, D.A.L. Lourenco, B.O. Fragomeni, and I. Aguilar. 2017. Technical note: Avoiding the direct inversion of the numerator relationship matrix for genotyped animals in single-step genomic best linear unbiased prediction solved with the preconditioned conjugate gradient. J. Dairy Sci. 95:49-52.

Misztal, I. 2017. Breeding and Genetics Symposium: Resilience and lessons from studies in genetics of heat stress. J. Anim. Sci. 95:1780–1787.

Silva, R.M.O., N.B. Stafuzza, B.O. Fragomeni, G.M.F. de Camargo, T.M. Ceacero, J.N.S.G. Cyrillo, F. Baldi, A.A. Boligon, M.E.Z. Mercadante, D.L. Lourenco,  I. Misztal, and L.G. Albuquerque. 2017. Genome-wide association study for carcass traits in an experimental Nelore cattle population. PloS One 12:e0169860.

Garcia-Baccino, C.A., A. Legarra, O.F. Christensen, I. Misztal, I. Pocrnic, Z.G. Vitezica, and R.J.C. Cantet. 2017. Metafounders are related to Fst fixation indices and reduce bias in single-step genomic evaluations. Genet. Sel. Evol. 49:34.

Bradford, H.L., I. Pocrnić, B.O. Fragomeni, D.A.L. Lourenco, and I. Misztal. 2017. Selection of core animals in the Algorithm for Proven and Young using a simulation model. J. Anim. Breed. Genet. 134:(doi: 10.1111/jbg.12276).            

Tsuruta, S. D. A. L. Lourenco, I. Misztal, and T. J. Lawlor. 2017. Genomic analysis of cow mortality and milk production using a threshold-linear model. J. Dairy Sci. 100:7295–7305. doi.org/10.3168/jds.2017-12665.

Pocrnic,I, D. A. L. Lourenco, H. L. Bradford, C. Y. Chen, and I. Misztal. 2017. Technical note: Impact of pedigree depth on convergence of single-step genomic BLUP in a purebred swine population. J. Animal. Sci. 95:3391-3395. doi: 10.2527/jas.2017.1581.

Fragomeni, B., D. A. L. Lourenco, Y. Masuda, A. Legarra, and I. Misztal. 2017. Incorporation of Causative Quantitative Trait Nucleotides in Single-step GBLUP. Genet. Sel. Evol. 49:1. (doi: 10.1186/s12711-017-0335-0.

Lourenco, D.A.L., B.O. Fragomeni, H. L. Bradford, I.R. Menezes, J.B.S. Ferraz, S. Tsuruta, I. Aguilar, and I. Misztal. 2017. Implications of SNP weighting on single-step genomic predictions for different reference population sizes. J. Anim. Bred. Genet. 10.1111/jbg.12288.

Masuda, Y., P. M. VanRaden, I. Misztal, and T. J. Lawlor. 2017. Differing genetic trend estimates from traditional and genomic evaluations for genotyped animals as evidence of pre-selection bias in US Holsteins. J. Dairy Sci. 101:1-113. J. Dairy Sci. 101:1–13. (doi:10.3168/jds.2017-13310)                 

Zhang, X., D. A. L. Lourenco, I. Aguilar, A. Legarra, R. J. Hawken, R. L. Sapp, and I. Misztal. Relationships among mortality, performance, and disorder traits in broiler chickens: a genetic and genomic approach. 2018. Poultry Sci. pex431, https://doi.org/10.3382/ps/pex431

Guarini, A.. D. A. L. Lourenco, L. Brito, M. Sargolzaei, C. Baes, F. Miglior, I. Misztal, and F. Schenkel. 2018. Comparison of genomic predictions for lowly heritable traits using multi-step and single-step genomic BLUP in Holstein cattle. J. Dairy Sci.  (In Print) https://doi.org/10.3168/jds.2017-14193

Garcia, A., B. Bosworth, G. Waldbieser, I. Misztal, S. Tsuruta, and D. Lourenco. 2018. Development of genomic predictions for harvest and carcass weight in channel catfish. Genet. Sel. Evol. 50:66. https://doi.org/10.1186/s12711-018-0435-5. 

Oliveira, D. P., D. A. L. Lourenco, S. Tsuruta, I. Misztal, D. J. A. Santos, F. R. de Araújo Neto, R. R. Aspilcueta-Borquis, F. Baldi, R. Carvalheiro, G. M. F. de Camargo, L. G. Albuquerque, and H. Tonhati. 2018. Reaction norm for yearling weight in beef cattle using single-step genomic evaluation, J. Anim. Sci. 96:27–34. https://doi.org/10.1093/jas/skx006.

Cesarani, A., I. Pocrnic, I. Misztal, N. Macciotta, and D.A.L. Lourenco. 2018. Bias in heritability estimates from genomic restricted maximum likelihood (GREML) methods under different genotyping strategies. J. Anim. Breed. Genet. doi: 10.1111/jbg.12367.

Guarini, A.. D. A. L. Lourenco, L. Brito, M. Sargolzaei, C. Baes, F. Miglior, I. Misztal, and F. Schenkel. Genetics and genomics of reproductive disorders in Holstein cattle. 2019. J.Dairy Sci. https://doi.org/10.3168/jds.2018-15038.

Bradford, H. L., Y. M. Masuda, P. M. VanRaden, A. Legarra, and I. Misztal. 2019. Modeling missing pedigree in single-step genomic BLUP. J. Dairy Sci. https://doi.org/10.3168/jds.2018-15434

Fragomeni, B., Y Masuda, H. L. Bradford, D.A.L. Lourenco, and I. Misztal. 2019. International bull evaluations by GBLUP with a prediction population. J. Dairy Sci. https://doi.org/10.3168/jds.2018-15554.

Bradford, H. L., Y. M. Masuda, J. B. Cole, I. Misztal, and P. M. VanRaden. 2019. Modeling pedigree accuracy and uncertain parentage in single-step evaluations of simulated and Holstein datasets. J. Dairy Sci. https://doi.org/10.3168/jds.2018-15419.

Hinayah, O., D. Lourenco, Y. Masuda, I. Misztal, S. Tsuruta, J. Jamrozik, L. Brito, F. Fonseca e Silva, and F. Schenkel. 2019. Application of single-step genomic evaluation using multiple-trait random regression test-day models in dairy cattle. J. Dairy Sci. https://doi.org/10.3168/jds.2018-15466.

Pocrnic, I., D. A. L. Lourenco,C. Y. Chen,W. O. Herring, and I. Misztal. 2019. Crossbred evaluations using single-step genomic BLUP and algorithm for proven and young with different sources of data. J. Anim. Sci.

Guarini, A.R., D.A.L. Lourenco, L.F. Brito, M. Sargolzaei, C. Baes, F. Miglior, S. Tsuruta, I. Misztal, and F. Schenkel. Use of a single-step approach for integration of external information into a national genomic evaluation for Holstein cattle in Canada. J. Dairy Sci.

Andonov, S., C. Costa, A. Uzunov, P. Bergomi, D.A.L. Lourenco, and I. Misztal. Modeling honey yield and defensive and swarming behaviors of Italian honey bees (Apis mellifera ligustica) using linear-threshold approaches. BMC Genomics.